Products related to Algorithms:
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Machine Learning Algorithms in Depth
Develop a mathematical intuition around machine learning algorithms to improve model performance and effectively troubleshoot complex ML problems.For intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus. Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you will learn the fundamentals of Bayesian inference and deep learning.You will also explore the core data structures and algorithmic paradigms for machine learning. You will explore practical implementations of dozens of ML algorithms, including: Monte Carlo Stock Price SimulationImage Denoising using Mean-Field Variational InferenceEM algorithm for Hidden Markov ModelsImbalanced Learning, Active Learning and Ensemble LearningBayesian Optimisation for Hyperparameter TuningDirichlet Process K-Means for Clustering ApplicationsStock Clusters based on Inverse Covariance EstimationEnergy Minimisation using Simulated AnnealingImage Search based on ResNet Convolutional Neural NetworkAnomaly Detection in Time-Series using Variational Autoencoders Each algorithm is fully explored with both math and practical implementations so you can see how they work and put into action. About the technology Fully understanding how machine learning algorithms function is essential for any serious ML engineer.This vital knowledge lets you modify algorithms to your specific needs, understand the trade-offs when picking an algorithm for a project, and better interpret and explain your results to your stakeholders.This unique guide will take you from relying on one-size-fits-all ML libraries to developing your own algorithms to solve your business needs.
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Teaching, Tutoring and Training in the Lifelong Learning Sector
This core text provides comprehensive support for pre-service and in-service trainee teachers in the Lifelong Learning Sector covering all they need to know to achieve QTLS status. Supporting trainees through all stages of their professional development, the text takes the reader through the theoretical background underpinning teaching and learning and offers practical guidance on day-to-day challenges. This fourth edition has been fully revised and updated and includes a new chapter on teaching practice with notes on observation and lesson planning.New information on behaviour management has been added to support trainees in an aspect of teaching that many find challenging.
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Information Theory, Inference and Learning Algorithms
Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography.The book introduces theory in tandem with applications.Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction.Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks.Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast.Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses.It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
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Computer Vision : Principles, Algorithms, Applications, Learning
Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints.This fully revised fifth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject. See an interview with the author explaining his approach to teaching and learning computer vision - http://scitechconnect.elsevier.com/computer-vision/
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What do algorithms achieve?
Algorithms achieve the ability to process and analyze large amounts of data quickly and efficiently. They help in making predictions, identifying patterns, and solving complex problems. Algorithms are used in various fields such as finance, healthcare, and technology to optimize processes and improve decision-making. Overall, algorithms play a crucial role in automating tasks, improving productivity, and driving innovation.
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What do algorithms calculate?
Algorithms are designed to calculate specific tasks or operations based on a set of instructions. They can be used to perform mathematical calculations, process data, analyze patterns, make decisions, and solve problems. In essence, algorithms are used to automate and streamline various processes by following a predefined sequence of steps to produce a desired outcome.
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What are the Instagram algorithms?
The Instagram algorithms are a set of complex calculations used by the platform to determine what content users see on their feed. These algorithms analyze user behavior, such as likes, comments, and shares, to prioritize content from accounts that users engage with the most. The algorithms also take into account the timeliness of posts, the relationship between users, and the type of content being shared. By using these algorithms, Instagram aims to show users the most relevant and engaging content on their feed.
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Which sorting algorithms are there?
There are several common sorting algorithms, including bubble sort, selection sort, insertion sort, merge sort, quick sort, and heap sort. Each algorithm has its own advantages and disadvantages in terms of time complexity, space complexity, and stability. The choice of sorting algorithm depends on the specific requirements of the problem at hand.
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Inside Deep Learning: Math, Algorithms, Models
"If you want to learn some of the deeper explanations of deep learning and PyTorch then read this book!" - Tiklu Ganguly Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorchSelect the right deep learning componentsTrain and evaluate a deep learning modelFine tune deep learning models to maximize performanceUnderstand deep learning terminologyAdapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework.It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field.No detail is skipped-you'll dive into math, theory, and practical applications.Everything is clearly explained in plain English. about the technologyDeep learning isn't just for big tech companies and academics.Anyone who needs to find meaningful insights and patterns in their data can benefit from these practical techniques!The unique ability for your systems to learn by example makes deep learning widely applicable across industries and use-cases, from filtering out spam to driving cars. about the bookInside Deep Learning is a fast-paced beginners' guide to solving common technical problems with deep learning.Written for everyday developers, there are no complex mathematical proofs or unnecessary academic theory.You'll learn how deep learning works through plain language, annotated code and equations as you work through dozens of instantly useful PyTorch examples. As you go, you'll build a French-English translator that works on the same principles as professional machine translation and discover cutting-edge techniques just emerging from the latest research.Best of all, every deep learning solution in this book can run in less than fifteen minutes using free GPU hardware! about the readerFor Python programmers with basic machine learning skills. about the authorEdward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library.His research includes deep learning, malware detection, reproducibility in ML, fairness/bias, and high performance computing.He is also a visiting professor at the University of Maryland, Baltimore County and teaches deep learning in the Data Science department.Dr Raff has over 40 peer reviewed publications, three best paper awards, and has presented at numerous major conferences.
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Advanced Machine Learning : Fundamentals and algorithms
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Algorithms
Use your big monkey brain to do things that even your teachers can't do.With these books, you will talk to computers, create games, draw pictures and find information.Come on, code monkeys - let's write some code!
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Genetic Algorithms and Machine Learning for Programmers
Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning.Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters.Learn how to test your ML code and dive into even more advanced topics.If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes.Build a repertoire of algorithms, discovering terms and approaches that apply generally.Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions. Build genetic algorithms. Make nature-inspired swarms with ants, bees and particles. Create Monte Carlo simulations. Investigate cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection methods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems.Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer.See how the algorithms explore and learn by creating visualizations of each problem.Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas).Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray.These plotting and testing libraries are not required but their use will give you a fuller experience.Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.
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Should one learn without algorithms?
Learning without algorithms is certainly possible, as there are many different ways to acquire knowledge and skills. However, algorithms can be valuable tools for organizing and processing information, so learning about them can be beneficial. Understanding algorithms can help individuals solve complex problems, improve decision-making processes, and enhance their overall problem-solving abilities. Therefore, while it is not necessary to learn algorithms, doing so can certainly be advantageous in many fields.
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What are simple algorithms in Java?
Simple algorithms in Java are step-by-step procedures for solving a specific problem or performing a specific task. These algorithms are typically written in Java programming language and are designed to be easy to understand and implement. Examples of simple algorithms in Java include sorting algorithms like bubble sort or insertion sort, searching algorithms like linear search or binary search, and mathematical algorithms like finding the factorial of a number or calculating the Fibonacci sequence. These algorithms are fundamental building blocks in computer science and are essential for solving a wide range of problems in software development.
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How do logarithmic sorting algorithms work?
Logarithmic sorting algorithms work by dividing the input data into smaller subgroups and recursively sorting these subgroups. One common example is the merge sort algorithm, which divides the input list into two halves, sorts each half separately, and then merges them back together in sorted order. By repeatedly dividing the data and merging the sorted subgroups, logarithmic sorting algorithms achieve a time complexity of O(n log n), making them efficient for large datasets.
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What are algorithms in computer science?
Algorithms in computer science are step-by-step procedures or formulas for solving a problem or accomplishing a task. They are a set of rules or instructions that are followed to achieve a particular goal. Algorithms are used in various computer science applications, such as sorting data, searching for information, and performing calculations. They are essential in programming and software development as they provide a systematic way to solve problems and process data efficiently.
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